The idea of using GANs to generate synthetic datasets is not new. It has been explored, for example in [1]} where synthetic Electronic Health Records are generated using a GAN. Their results show that the synthetic data is comparable to the real data in terms of distribution statistics, performance in predictive modeling tasks, and by verification by an expert review. They observe an empirical privacy gain from their method. An advancement of this idea was made by [2]} with their algorithm PATE-GAN, which quantified a privacy gain by introducing a layer of differential privacy. Their algorithm was based around a "student" GAN discriminator which was taught to label synthetic examples as "real" or "fake" based on the predictions of an array of "parent" discriminators. The parent discriminators are trained on sections of the real data. The student however is trained on synthetic data which has been labelled real or fake using a differentially private vote aggregation from the parent discriminators. The generator is trained using only the differentially private discriminator, so by the post-processing rule of differential privacy, the generated dataset has the same privacy guarantees as the private discriminator [3]}. The results work well using a variety of different datasets. The method has not be shown to work well with image datasets however, possibly due to the higher complexity of the data.
